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classmatplotlib.scale.InvertedLogTransform(base)[source]
Bases: matplotlib.transforms.Transform Parameters
shorthand_namestr
A string representing the "name" of the transform. The name carries no significance other than to improve the readability of str(transform) when DEBUG=True. has_inverse=True
True ... | matplotlib.scale_api#matplotlib.scale.InvertedLogTransform |
has_inverse=True
True if this transform has a corresponding inverse transform. | matplotlib.scale_api#matplotlib.scale.InvertedLogTransform.has_inverse |
input_dims=1
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass. | matplotlib.scale_api#matplotlib.scale.InvertedLogTransform.input_dims |
inverted()[source]
Return the corresponding inverse transformation. It holds x == self.inverted().transform(self.transform(x)). The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy. | matplotlib.scale_api#matplotlib.scale.InvertedLogTransform.inverted |
is_separable=True
True if this transform is separable in the x- and y- dimensions. | matplotlib.scale_api#matplotlib.scale.InvertedLogTransform.is_separable |
output_dims=1
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass. | matplotlib.scale_api#matplotlib.scale.InvertedLogTransform.output_dims |
transform_non_affine(a)[source]
Apply only the non-affine part of this transformation. transform(values) is always equivalent to transform_affine(transform_non_affine(values)). In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op. Paramet... | matplotlib.scale_api#matplotlib.scale.InvertedLogTransform.transform_non_affine |
classmatplotlib.scale.InvertedSymmetricalLogTransform(base, linthresh, linscale)[source]
Bases: matplotlib.transforms.Transform Parameters
shorthand_namestr
A string representing the "name" of the transform. The name carries no significance other than to improve the readability of str(transform) when DEBUG=True... | matplotlib.scale_api#matplotlib.scale.InvertedSymmetricalLogTransform |
has_inverse=True
True if this transform has a corresponding inverse transform. | matplotlib.scale_api#matplotlib.scale.InvertedSymmetricalLogTransform.has_inverse |
input_dims=1
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass. | matplotlib.scale_api#matplotlib.scale.InvertedSymmetricalLogTransform.input_dims |
inverted()[source]
Return the corresponding inverse transformation. It holds x == self.inverted().transform(self.transform(x)). The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy. | matplotlib.scale_api#matplotlib.scale.InvertedSymmetricalLogTransform.inverted |
is_separable=True
True if this transform is separable in the x- and y- dimensions. | matplotlib.scale_api#matplotlib.scale.InvertedSymmetricalLogTransform.is_separable |
output_dims=1
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass. | matplotlib.scale_api#matplotlib.scale.InvertedSymmetricalLogTransform.output_dims |
transform_non_affine(a)[source]
Apply only the non-affine part of this transformation. transform(values) is always equivalent to transform_affine(transform_non_affine(values)). In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op. Paramet... | matplotlib.scale_api#matplotlib.scale.InvertedSymmetricalLogTransform.transform_non_affine |
classmatplotlib.scale.LinearScale(axis)[source]
Bases: matplotlib.scale.ScaleBase The default linear scale. get_transform()[source]
Return the transform for linear scaling, which is just the IdentityTransform.
name='linear'
set_default_locators_and_formatters(axis)[source]
Set the locators and formatter... | matplotlib.scale_api#matplotlib.scale.LinearScale |
get_transform()[source]
Return the transform for linear scaling, which is just the IdentityTransform. | matplotlib.scale_api#matplotlib.scale.LinearScale.get_transform |
name='linear' | matplotlib.scale_api#matplotlib.scale.LinearScale.name |
set_default_locators_and_formatters(axis)[source]
Set the locators and formatters of axis to instances suitable for this scale. | matplotlib.scale_api#matplotlib.scale.LinearScale.set_default_locators_and_formatters |
classmatplotlib.scale.LogisticTransform(nonpositive='mask')[source]
Bases: matplotlib.transforms.Transform Parameters
shorthand_namestr
A string representing the "name" of the transform. The name carries no significance other than to improve the readability of str(transform) when DEBUG=True. has_inverse=T... | matplotlib.scale_api#matplotlib.scale.LogisticTransform |
has_inverse=True
True if this transform has a corresponding inverse transform. | matplotlib.scale_api#matplotlib.scale.LogisticTransform.has_inverse |
input_dims=1
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass. | matplotlib.scale_api#matplotlib.scale.LogisticTransform.input_dims |
inverted()[source]
Return the corresponding inverse transformation. It holds x == self.inverted().transform(self.transform(x)). The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy. | matplotlib.scale_api#matplotlib.scale.LogisticTransform.inverted |
is_separable=True
True if this transform is separable in the x- and y- dimensions. | matplotlib.scale_api#matplotlib.scale.LogisticTransform.is_separable |
output_dims=1
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass. | matplotlib.scale_api#matplotlib.scale.LogisticTransform.output_dims |
transform_non_affine(a)[source]
logistic transform (base 10) | matplotlib.scale_api#matplotlib.scale.LogisticTransform.transform_non_affine |
classmatplotlib.scale.LogitScale(axis, nonpositive='mask', *, one_half='\x0crac{1}{2}', use_overline=False)[source]
Bases: matplotlib.scale.ScaleBase Logit scale for data between zero and one, both excluded. This scale is similar to a log scale close to zero and to one, and almost linear around 0.5. It maps the inter... | matplotlib.scale_api#matplotlib.scale.LogitScale |
get_transform()[source]
Return the LogitTransform associated with this scale. | matplotlib.scale_api#matplotlib.scale.LogitScale.get_transform |
limit_range_for_scale(vmin, vmax, minpos)[source]
Limit the domain to values between 0 and 1 (excluded). | matplotlib.scale_api#matplotlib.scale.LogitScale.limit_range_for_scale |
name='logit' | matplotlib.scale_api#matplotlib.scale.LogitScale.name |
set_default_locators_and_formatters(axis)[source]
Set the locators and formatters of axis to instances suitable for this scale. | matplotlib.scale_api#matplotlib.scale.LogitScale.set_default_locators_and_formatters |
classmatplotlib.scale.LogitTransform(nonpositive='mask')[source]
Bases: matplotlib.transforms.Transform Parameters
shorthand_namestr
A string representing the "name" of the transform. The name carries no significance other than to improve the readability of str(transform) when DEBUG=True. has_inverse=True... | matplotlib.scale_api#matplotlib.scale.LogitTransform |
has_inverse=True
True if this transform has a corresponding inverse transform. | matplotlib.scale_api#matplotlib.scale.LogitTransform.has_inverse |
input_dims=1
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass. | matplotlib.scale_api#matplotlib.scale.LogitTransform.input_dims |
inverted()[source]
Return the corresponding inverse transformation. It holds x == self.inverted().transform(self.transform(x)). The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy. | matplotlib.scale_api#matplotlib.scale.LogitTransform.inverted |
is_separable=True
True if this transform is separable in the x- and y- dimensions. | matplotlib.scale_api#matplotlib.scale.LogitTransform.is_separable |
output_dims=1
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass. | matplotlib.scale_api#matplotlib.scale.LogitTransform.output_dims |
transform_non_affine(a)[source]
logit transform (base 10), masked or clipped | matplotlib.scale_api#matplotlib.scale.LogitTransform.transform_non_affine |
classmatplotlib.scale.LogScale(axis, *, base=10, subs=None, nonpositive='clip')[source]
Bases: matplotlib.scale.ScaleBase A standard logarithmic scale. Care is taken to only plot positive values. Parameters
axisAxis
The axis for the scale.
basefloat, default: 10
The base of the logarithm.
nonpositive{'cli... | matplotlib.scale_api#matplotlib.scale.LogScale |
get_transform()[source]
Return the LogTransform associated with this scale. | matplotlib.scale_api#matplotlib.scale.LogScale.get_transform |
limit_range_for_scale(vmin, vmax, minpos)[source]
Limit the domain to positive values. | matplotlib.scale_api#matplotlib.scale.LogScale.limit_range_for_scale |
name='log' | matplotlib.scale_api#matplotlib.scale.LogScale.name |
set_default_locators_and_formatters(axis)[source]
Set the locators and formatters of axis to instances suitable for this scale. | matplotlib.scale_api#matplotlib.scale.LogScale.set_default_locators_and_formatters |
classmatplotlib.scale.LogTransform(base, nonpositive='clip')[source]
Bases: matplotlib.transforms.Transform Parameters
shorthand_namestr
A string representing the "name" of the transform. The name carries no significance other than to improve the readability of str(transform) when DEBUG=True. has_inverse=... | matplotlib.scale_api#matplotlib.scale.LogTransform |
has_inverse=True
True if this transform has a corresponding inverse transform. | matplotlib.scale_api#matplotlib.scale.LogTransform.has_inverse |
input_dims=1
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass. | matplotlib.scale_api#matplotlib.scale.LogTransform.input_dims |
inverted()[source]
Return the corresponding inverse transformation. It holds x == self.inverted().transform(self.transform(x)). The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy. | matplotlib.scale_api#matplotlib.scale.LogTransform.inverted |
is_separable=True
True if this transform is separable in the x- and y- dimensions. | matplotlib.scale_api#matplotlib.scale.LogTransform.is_separable |
output_dims=1
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass. | matplotlib.scale_api#matplotlib.scale.LogTransform.output_dims |
transform_non_affine(a)[source]
Apply only the non-affine part of this transformation. transform(values) is always equivalent to transform_affine(transform_non_affine(values)). In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op. Paramet... | matplotlib.scale_api#matplotlib.scale.LogTransform.transform_non_affine |
matplotlib.scale.register_scale(scale_class)[source]
Register a new kind of scale. Parameters
scale_classsubclass of ScaleBase
The scale to register. | matplotlib.scale_api#matplotlib.scale.register_scale |
matplotlib.scale.scale_factory(scale, axis, **kwargs)[source]
Return a scale class by name. Parameters
scale{'function', 'functionlog', 'linear', 'log', 'logit', 'symlog'}
axismatplotlib.axis.Axis | matplotlib.scale_api#matplotlib.scale.scale_factory |
classmatplotlib.scale.ScaleBase(axis)[source]
Bases: object The base class for all scales. Scales are separable transformations, working on a single dimension. Subclasses should override name
The scale's name. get_transform()
A method returning a Transform, which converts data coordinates to scaled coordinates. T... | matplotlib.scale_api#matplotlib.scale.ScaleBase |
get_transform()[source]
Return the Transform object associated with this scale. | matplotlib.scale_api#matplotlib.scale.ScaleBase.get_transform |
limit_range_for_scale(vmin, vmax, minpos)[source]
Return the range vmin, vmax, restricted to the domain supported by this scale (if any). minpos should be the minimum positive value in the data. This is used by log scales to determine a minimum value. | matplotlib.scale_api#matplotlib.scale.ScaleBase.limit_range_for_scale |
set_default_locators_and_formatters(axis)[source]
Set the locators and formatters of axis to instances suitable for this scale. | matplotlib.scale_api#matplotlib.scale.ScaleBase.set_default_locators_and_formatters |
classmatplotlib.scale.SymmetricalLogScale(axis, *, base=10, linthresh=2, subs=None, linscale=1)[source]
Bases: matplotlib.scale.ScaleBase The symmetrical logarithmic scale is logarithmic in both the positive and negative directions from the origin. Since the values close to zero tend toward infinity, there is a need ... | matplotlib.scale_api#matplotlib.scale.SymmetricalLogScale |
get_transform()[source]
Return the SymmetricalLogTransform associated with this scale. | matplotlib.scale_api#matplotlib.scale.SymmetricalLogScale.get_transform |
name='symlog' | matplotlib.scale_api#matplotlib.scale.SymmetricalLogScale.name |
set_default_locators_and_formatters(axis)[source]
Set the locators and formatters of axis to instances suitable for this scale. | matplotlib.scale_api#matplotlib.scale.SymmetricalLogScale.set_default_locators_and_formatters |
classmatplotlib.scale.SymmetricalLogTransform(base, linthresh, linscale)[source]
Bases: matplotlib.transforms.Transform Parameters
shorthand_namestr
A string representing the "name" of the transform. The name carries no significance other than to improve the readability of str(transform) when DEBUG=True. ... | matplotlib.scale_api#matplotlib.scale.SymmetricalLogTransform |
has_inverse=True
True if this transform has a corresponding inverse transform. | matplotlib.scale_api#matplotlib.scale.SymmetricalLogTransform.has_inverse |
input_dims=1
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass. | matplotlib.scale_api#matplotlib.scale.SymmetricalLogTransform.input_dims |
inverted()[source]
Return the corresponding inverse transformation. It holds x == self.inverted().transform(self.transform(x)). The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy. | matplotlib.scale_api#matplotlib.scale.SymmetricalLogTransform.inverted |
is_separable=True
True if this transform is separable in the x- and y- dimensions. | matplotlib.scale_api#matplotlib.scale.SymmetricalLogTransform.is_separable |
output_dims=1
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass. | matplotlib.scale_api#matplotlib.scale.SymmetricalLogTransform.output_dims |
transform_non_affine(a)[source]
Apply only the non-affine part of this transformation. transform(values) is always equivalent to transform_affine(transform_non_affine(values)). In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op. Paramet... | matplotlib.scale_api#matplotlib.scale.SymmetricalLogTransform.transform_non_affine |
matplotlib.set_loglevel(level)[source]
Set Matplotlib's root logger and root logger handler level, creating the handler if it does not exist yet. Typically, one should call set_loglevel("info") or set_loglevel("debug") to get additional debugging information. Parameters
level{"notset", "debug", "info", "warning",... | matplotlib_configuration_api#matplotlib.set_loglevel |
matplotlib.sphinxext.mathmpl A role and directive to display mathtext in Sphinx Warning In most cases, you will likely want to use one of Sphinx's builtin Math extensions instead of this one. Mathtext may be included in two ways:
Inline, using the role: This text uses inline math: :mathmpl:`\alpha > \beta`.
which... | matplotlib.sphinxext_mathmpl_api |
classmatplotlib.sphinxext.mathmpl.MathDirective(name, arguments, options, content, lineno, content_offset, block_text, state, state_machine)[source]
The .. mathmpl:: directive, as documented in the module's docstring. | matplotlib.sphinxext_mathmpl_api#matplotlib.sphinxext.mathmpl.MathDirective |
matplotlib.sphinxext.plot_directive A directive for including a Matplotlib plot in a Sphinx document By default, in HTML output, plot will include a .png file with a link to a high-res .png and .pdf. In LaTeX output, it will include a .pdf. The source code for the plot may be included in one of three ways:
A path to... | matplotlib.sphinxext_plot_directive_api |
matplotlib.sphinxext.plot_directive.mark_plot_labels(app, document)[source]
To make plots referenceable, we need to move the reference from the "htmlonly" (or "latexonly") node to the actual figure node itself. | matplotlib.sphinxext_plot_directive_api#matplotlib.sphinxext.plot_directive.mark_plot_labels |
matplotlib.sphinxext.plot_directive.out_of_date(original, derived, includes=None)[source]
Return whether derived is out-of-date relative to original or any of the RST files included in it using the RST include directive (includes). derived and original are full paths, and includes is optionally a list of full paths w... | matplotlib.sphinxext_plot_directive_api#matplotlib.sphinxext.plot_directive.out_of_date |
classmatplotlib.sphinxext.plot_directive.PlotDirective(name, arguments, options, content, lineno, content_offset, block_text, state, state_machine)[source]
The .. plot:: directive, as documented in the module's docstring. run()[source]
Run the plot directive. | matplotlib.sphinxext_plot_directive_api#matplotlib.sphinxext.plot_directive.PlotDirective |
run()[source]
Run the plot directive. | matplotlib.sphinxext_plot_directive_api#matplotlib.sphinxext.plot_directive.PlotDirective.run |
exceptionmatplotlib.sphinxext.plot_directive.PlotError[source] | matplotlib.sphinxext_plot_directive_api#matplotlib.sphinxext.plot_directive.PlotError |
matplotlib.sphinxext.plot_directive.render_figures(code, code_path, output_dir, output_base, context, function_name, config, context_reset=False, close_figs=False, code_includes=None)[source]
Run a pyplot script and save the images in output_dir. Save the images under output_dir with file names derived from output_ba... | matplotlib.sphinxext_plot_directive_api#matplotlib.sphinxext.plot_directive.render_figures |
matplotlib.sphinxext.plot_directive.run_code(code, code_path, ns=None, function_name=None)[source]
[Deprecated] Import a Python module from a path, and run the function given by name, if function_name is not None. Notes Deprecated since version 3.5. | matplotlib.sphinxext_plot_directive_api#matplotlib.sphinxext.plot_directive.run_code |
matplotlib.sphinxext.plot_directive.split_code_at_show(text)[source]
[Deprecated] Split code at plt.show(). Notes Deprecated since version 3.5. | matplotlib.sphinxext_plot_directive_api#matplotlib.sphinxext.plot_directive.split_code_at_show |
matplotlib.sphinxext.plot_directive.unescape_doctest(text)[source]
[Deprecated] Extract code from a piece of text, which contains either Python code or doctests. Notes Deprecated since version 3.5. | matplotlib.sphinxext_plot_directive_api#matplotlib.sphinxext.plot_directive.unescape_doctest |
matplotlib.spines classmatplotlib.spines.Spine(axes, spine_type, path, **kwargs)[source]
Bases: matplotlib.patches.Patch An axis spine -- the line noting the data area boundaries. Spines are the lines connecting the axis tick marks and noting the boundaries of the data area. They can be placed at arbitrary position... | matplotlib.spines_api |
classmatplotlib.spines.Spine(axes, spine_type, path, **kwargs)[source]
Bases: matplotlib.patches.Patch An axis spine -- the line noting the data area boundaries. Spines are the lines connecting the axis tick marks and noting the boundaries of the data area. They can be placed at arbitrary positions. See set_position ... | matplotlib.spines_api#matplotlib.spines.Spine |
classmethodarc_spine(axes, spine_type, center, radius, theta1, theta2, **kwargs)[source]
Create and return an arc Spine. | matplotlib.spines_api#matplotlib.spines.Spine.arc_spine |
classmethodcircular_spine(axes, center, radius, **kwargs)[source]
Create and return a circular Spine. | matplotlib.spines_api#matplotlib.spines.Spine.circular_spine |
cla()[source]
[Deprecated] Notes Deprecated since version 3.4: | matplotlib.spines_api#matplotlib.spines.Spine.cla |
clear()[source]
Clear the current spine. | matplotlib.spines_api#matplotlib.spines.Spine.clear |
draw(renderer)[source]
Draw the Artist (and its children) using the given renderer. This has no effect if the artist is not visible (Artist.get_visible returns False). Parameters
rendererRendererBase subclass.
Notes This method is overridden in the Artist subclasses. | matplotlib.spines_api#matplotlib.spines.Spine.draw |
get_bounds()[source]
Get the bounds of the spine. | matplotlib.spines_api#matplotlib.spines.Spine.get_bounds |
get_patch_transform()[source]
Return the Transform instance mapping patch coordinates to data coordinates. For example, one may define a patch of a circle which represents a radius of 5 by providing coordinates for a unit circle, and a transform which scales the coordinates (the patch coordinate) by 5. | matplotlib.spines_api#matplotlib.spines.Spine.get_patch_transform |
get_path()[source]
Return the path of this patch. | matplotlib.spines_api#matplotlib.spines.Spine.get_path |
get_position()[source]
Return the spine position. | matplotlib.spines_api#matplotlib.spines.Spine.get_position |
get_spine_transform()[source]
Return the spine transform. | matplotlib.spines_api#matplotlib.spines.Spine.get_spine_transform |
get_window_extent(renderer=None)[source]
Return the window extent of the spines in display space, including padding for ticks (but not their labels) See also matplotlib.axes.Axes.get_tightbbox
matplotlib.axes.Axes.get_window_extent | matplotlib.spines_api#matplotlib.spines.Spine.get_window_extent |
classmethodlinear_spine(axes, spine_type, **kwargs)[source]
Create and return a linear Spine. | matplotlib.spines_api#matplotlib.spines.Spine.linear_spine |
register_axis(axis)[source]
Register an axis. An axis should be registered with its corresponding spine from the Axes instance. This allows the spine to clear any axis properties when needed. | matplotlib.spines_api#matplotlib.spines.Spine.register_axis |
set(*, agg_filter=<UNSET>, alpha=<UNSET>, animated=<UNSET>, antialiased=<UNSET>, bounds=<UNSET>, capstyle=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, color=<UNSET>, edgecolor=<UNSET>, facecolor=<UNSET>, fill=<UNSET>, gid=<UNSET>, hatch=<UNSET>, in_layout=<UNSET>, joinstyle=<UNSET>, label=<UNSET>, lin... | matplotlib.spines_api#matplotlib.spines.Spine.set |
set_bounds(low=None, high=None)[source]
Set the spine bounds. Parameters
lowfloat or None, optional
The lower spine bound. Passing None leaves the limit unchanged. The bounds may also be passed as the tuple (low, high) as the first positional argument.
highfloat or None, optional
The higher spine bound. Pas... | matplotlib.spines_api#matplotlib.spines.Spine.set_bounds |
set_color(c)[source]
Set the edgecolor. Parameters
ccolor
Notes This method does not modify the facecolor (which defaults to "none"), unlike the Patch.set_color method defined in the parent class. Use Patch.set_facecolor to set the facecolor. | matplotlib.spines_api#matplotlib.spines.Spine.set_color |
set_patch_arc(center, radius, theta1, theta2)[source]
Set the spine to be arc-like. | matplotlib.spines_api#matplotlib.spines.Spine.set_patch_arc |
set_patch_circle(center, radius)[source]
Set the spine to be circular. | matplotlib.spines_api#matplotlib.spines.Spine.set_patch_circle |
set_patch_line()[source]
Set the spine to be linear. | matplotlib.spines_api#matplotlib.spines.Spine.set_patch_line |
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